#
# Copyright 2016 The BigDL Authors.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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#
# Some parts of this file is adapted from
# https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
# which is licensed under Apache License 2.0:
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# Copyright 2024 The HuggingFace Team. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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import math
import torch
from typing import Optional

from ipex_llm.transformers.utils import get_xpu_device_type
from ipex_llm.transformers.models.common import padding_qkv_hd
from ipex_llm.transformers.models.common import scaled_dot_product_attention
from diffusers.models.attention_processor import Attention


class AttnProcessor2_0:
    r"""
    Processor for implementing scaled dot-product attention.
    """

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.Tensor,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        temb: Optional[torch.Tensor] = None,
        *args,
        **kwargs,
    ) -> torch.Tensor:
        residual = hidden_states
        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask,
                                                         sequence_length, batch_size)
            # scaled_dot_product_attention expects attention_mask shape to be
            # (batch, heads, source_length, target_length)
            attention_mask = attention_mask.view(batch_size, attn.heads,
                                                 -1, attention_mask.shape[-1])

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        if attn.norm_q is not None:
            query = attn.norm_q(query)
        if attn.norm_k is not None:
            key = attn.norm_k(key)

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        # IPEX-LLM changes start
        if query.device.type == "xpu" and query.dtype in [torch.half, torch.float]:
            # padding head_dim 40 to 64
            query, key, value = padding_qkv_hd(query, key, value, 40, 64)
            hidden_states = scaled_dot_product_attention(
                query, key.contiguous(), value.contiguous(),
                attention_mask, False, 1 / math.sqrt(head_dim)
            )
            hidden_states = hidden_states[:, :, :, :head_dim]
        else:
            hidden_states = torch.nn.functional.scaled_dot_product_attention(
                query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
            )
        # IPEX-LLM changes end

        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1,
                                                              attn.heads * head_dim)
        hidden_states = hidden_states.to(query.dtype)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel,
                                                                    height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states


def upcast_vae(self):
    # workaround overflow and ipex's bugs
    if get_xpu_device_type(self.vae.post_quant_conv.weight) in ["arc", "flex", "pvc"]:
        self.vae.to(torch.bfloat16)
    else:
        self.vae.decoder.up_blocks.to(torch.bfloat16)
        self.vae.decoder.conv_norm_out.to(torch.bfloat16)
        self.vae.decoder.conv_out.to(torch.bfloat16)